2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-1.8
Paper Title LOCOP: LOCAL COLLABORATIVE OBJECT PRESENCE FOR SEMANTIC LABELING VIA SCORE MAP RE-INFERENCE
Authors Lin Guo, Guoliang Fan, Oklahoma State University, United States
SessionARS-1: Object Detection
LocationArea I
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recent research has focused on end-to-end networks for indoor scene semantic labeling. However, in addition to learning bottom-up features, high-level knowledge could be implemented to guide the local classification. In this paper, we take advantage of trained semantic labeling networks by using the intermediate layer output as a per-category local detector and implement the context information in a network structure to boost the semantic segmentation performance. A deep learning-based re-inferencing frame work is proposed to boost any pixel-level labeling outputs using our local collaborative object presence (LoCOP) feature as the global-to-local guidance. Experimental results show that the detection accuracy is improved with our re-inference approach.